Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Downloading mnist: 9.92MB [00:00, 24.2MB/s]                            
Extracting mnist: 100%|██████████| 60.0K/60.0K [00:09<00:00, 6.65KFile/s]
Downloading celeba: 1.44GB [01:31, 15.9MB/s]                               
Extracting celeba...

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7fd185f1e208>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7fd185e0a048>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='inputs_real')
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='inputs_z')
    learning_rate = tf.placeholder(tf.float32)
    
    return inputs_real, inputs_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [25]:
def leaky_relu(tensor, alpha=0.1):
    return tf.maximum(tensor * alpha, tensor)
In [101]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    with tf.variable_scope('discriminator', reuse=reuse):
        
        # Use a stride of two to reduce size.
        # Nothing special otherwise.
        conv1 = tf.layers.conv2d(images, 32, 3, 1, padding='same', activation=None, kernel_initializer=tf.contrib.layers.xavier_initializer())
        conv1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[3, 3], strides=2)
        conv1 = tf.layers.batch_normalization(conv1, training=True)
        conv1 = leaky_relu(conv1)
        #conv1 = tf.layers.dropout(conv1, 0.5)

        # Just another convolution layer. Increasing the filter count.
        conv2 = tf.layers.conv2d(conv1, 64, 3, 1, padding='same', activation=None, kernel_initializer=tf.contrib.layers.xavier_initializer())
        conv2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[3, 3], strides=2)
        conv2 = tf.layers.batch_normalization(conv2, training=True)
        conv2 = leaky_relu(conv2)
        #conv2 = tf.layers.dropout(conv2, 0.5)

        # A convolution layer without downsizing
        conv3 = tf.layers.conv2d(conv2, 64, 7, padding='same', activation=None, kernel_initializer=tf.contrib.layers.xavier_initializer())
        conv3 = tf.layers.batch_normalization(conv3, training=True)
        conv3 = leaky_relu(conv3)
        #conv3 = tf.layers.dropout(conv3, 0.5)
        
        #and another
        conv4 = tf.layers.conv2d(conv3, 64, 3, padding='same', activation=None, kernel_initializer=tf.contrib.layers.xavier_initializer())
        conv4 = tf.layers.batch_normalization(conv4, training=True)
        conv4 = leaky_relu(conv4)
        conv4 = tf.layers.dropout(conv4, 0.2)
        
        flattened = tf.contrib.layers.flatten(conv4)

        logits = tf.layers.dense(flattened, 1, activation=None, kernel_initializer=tf.contrib.layers.xavier_initializer())
        out = tf.sigmoid(logits)
        
        return out, logits

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [104]:
# adds batch normalization and leaky relu to a layer
def batch_normalize_and_activate(inputs, is_train):
    normalized = tf.layers.batch_normalization(inputs, training=is_train)
    with_activation = leaky_relu(normalized)
    
    return with_activation


def upsample_and_convolve(inputs, neighborhood_shape, filter_count, filter_shape, padding, is_train, normalize=True):
    resized = tf.image.resize_nearest_neighbor(inputs, neighborhood_shape)
    convolved = tf.layers.conv2d(resized, filter_count, filter_shape, padding=padding, activation=None)
    
    if normalize:
        convolved = batch_normalize_and_activate(convolved, is_train)
    else:
        convolved = leaky_relu(convolved)
        
    return convolved

def transpose_convolve(inputs, filter_count, is_train, kernel_size=4, strides=2, padding='same', normalize=True):
    convolved = tf.layers.conv2d_transpose(inputs, filter_count, kernel_size, strides=strides, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
    
    if normalize:
        convolved = batch_normalize_and_activate(convolved, is_train)
    
    return convolved
    

def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    with tf.variable_scope('generator', reuse = (not is_train)):
        # add a fully-connected layer
        hidden_layer_implicit_width = 12
        hidden_layer_size = 16 * hidden_layer_implicit_width * hidden_layer_implicit_width
        h1 = tf.layers.dense(z, hidden_layer_size, kernel_initializer=tf.contrib.layers.xavier_initializer())
        h1 = batch_normalize_and_activate(h1, is_train)
        
        # high dropout with this early wide layer prevents overfitting
        h1 = tf.layers.dropout(h1, 0.7, training=is_train)
        
        reshaped = tf.reshape(h1, [-1, hidden_layer_implicit_width, hidden_layer_implicit_width, hidden_layer_size // (hidden_layer_implicit_width * hidden_layer_implicit_width)])
        
        up1 = upsample_and_convolve(reshaped, (14, 14), 256, (3,3), 'same', is_train)
        up1 = tf.layers.dropout(up1, 0.5, training=is_train)
        
        up2 = upsample_and_convolve(up1, (16, 16), 256, (3,3), 'same', is_train)
        up2 = tf.layers.dropout(up2, 0.5, training=is_train)
        
        up3 = upsample_and_convolve(up2, (18, 18), 256, (3,3), 'same', is_train)
        up3 = tf.layers.dropout(up3, 0.5, training=is_train)
        
        up4 = upsample_and_convolve(up3, (20, 20), 256, (3,3), 'same', is_train)
        up4 = tf.layers.dropout(up4, 0.5, training=is_train)
        
        up5 = upsample_and_convolve(up4, (22, 22), 256, (7,7), 'same', is_train)
        up5 = tf.layers.dropout(up5, 0.5, training=is_train)
        
        up6 = upsample_and_convolve(up5, (24, 24), 256, (5,5), 'same', is_train)
        up6 = tf.layers.dropout(up6, 0.5, training=is_train)
        
        # I have found that removing batch normalization in late layers
        # helps prevent weird artifacts
        
        up7 = upsample_and_convolve(up6, (28, 28), 256, (4,4), 'same', is_train, normalize=False)
        up7 = tf.layers.dropout(up7, 0.5, training=is_train)
        
        # run a same-size convolution on the resulting output to sharpen it up
        same = tf.layers.conv2d(up7, 128, 4, padding='same', activation=None, kernel_initializer=tf.contrib.layers.xavier_initializer())
        #same = batch_normalize_and_activate(same, is_train)
        same = leaky_relu(same)
        same = tf.layers.dropout(same, 0.5, training=is_train)
        
        #and another one
        same = tf.layers.conv2d(same, 128, 2, padding='same', activation=None, kernel_initializer=tf.contrib.layers.xavier_initializer())
        #same = batch_normalize_and_activate(same, is_train)
        same = leaky_relu(same)
        same = tf.layers.dropout(same, 0.3, training=is_train)
        
        #and another
        same = tf.layers.conv2d(same, 128, 1, padding='same', activation=None, kernel_initializer=tf.contrib.layers.xavier_initializer())
        #same = batch_normalize_and_activate(same, is_train)
        same = leaky_relu(same)
        same = tf.layers.dropout(same, 0.1, training=is_train)
        
        # drop to the appropriate number of channels with a final convolution
        logits = tf.layers.conv2d(same, out_channel_dim, 1, padding='same', activation=None, kernel_initializer=tf.contrib.layers.xavier_initializer())
        
        out = tf.tanh(logits)
        
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [9]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    smooth = 0.1

    g_model = generator(input_z, out_channel_dim, is_train=True)
    
    d_model_real, d_logits_real = discriminator(input_real, reuse=False)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,
                                                                    labels=tf.ones_like(d_logits_real) * (1 - smooth)))

    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
                                                                    labels=tf.zeros_like(d_logits_fake)))

    d_loss = d_loss_real + d_loss_fake

    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
                                                               labels=tf.ones_like(d_logits_fake)))

    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [10]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # Get the trainable_variables, split into G and D parts
    t_vars = tf.trainable_variables()
    g_vars = [variable for variable in t_vars if variable.name.startswith('generator')]
    d_vars = [variable for variable in t_vars if variable.name.startswith('discriminator')]

    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
    
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [11]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [23]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    
    # model definition
    _, image_width, image_height, image_channels = list(data_shape)
    input_real, input_z, _ = model_inputs(image_width, image_height, image_channels, z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, image_channels)
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    # training
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            batch_number = 0
            for batch_images in get_batches(batch_size):
                batch_number += 1
                
                #scale properly
                batch_images = batch_images * 2
                
                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # Run optimizers
                _ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z})
                
                # the generator needs more training than the discriminator.
                # let it get there.
                train_iterations = 0
                train_loss_d = sess.run(d_loss, {input_z: batch_z, input_real: batch_images})
                while g_loss.eval({input_z: batch_z, input_real: batch_images}) > train_loss_d and train_iterations < 5:
                    _ = sess.run(g_train_opt, feed_dict={input_z: batch_z, input_real: batch_images})
                    train_iterations += 1
                
                # display sample images every 100 batches
                if batch_number % 100 == 0:
                    show_generator_output(sess, 25, input_z, image_channels, data_image_mode)
                    
                    # get the losses and print them out
                    train_loss_d = sess.run(d_loss, {input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z, input_real: batch_images})

                    print("Epoch {}/{} |".format(epoch_i + 1, epoch_count),
                          "Batch number {} |".format(batch_number),
                          "Discriminator Loss: {:.4f} |".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [106]:
batch_size = 32
z_dim = 128
learning_rate = 0.0001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

# might need to run this more than once :)
tf.reset_default_graph()

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2 | Batch number 100 | Discriminator Loss: 0.5940 | Generator Loss: 2.0594
Epoch 1/2 | Batch number 200 | Discriminator Loss: 0.5152 | Generator Loss: 3.0011
Epoch 1/2 | Batch number 300 | Discriminator Loss: 0.5858 | Generator Loss: 2.2258
Epoch 1/2 | Batch number 400 | Discriminator Loss: 0.5590 | Generator Loss: 2.3765
Epoch 1/2 | Batch number 500 | Discriminator Loss: 0.6254 | Generator Loss: 1.9542
Epoch 1/2 | Batch number 600 | Discriminator Loss: 0.5680 | Generator Loss: 2.7851
Epoch 1/2 | Batch number 700 | Discriminator Loss: 0.7348 | Generator Loss: 2.3907
Epoch 1/2 | Batch number 800 | Discriminator Loss: 0.6414 | Generator Loss: 3.2846
Epoch 1/2 | Batch number 900 | Discriminator Loss: 0.7366 | Generator Loss: 2.0682
Epoch 1/2 | Batch number 1000 | Discriminator Loss: 0.6243 | Generator Loss: 2.3349
Epoch 1/2 | Batch number 1100 | Discriminator Loss: 0.6453 | Generator Loss: 2.3008
Epoch 1/2 | Batch number 1200 | Discriminator Loss: 0.7821 | Generator Loss: 1.7073
Epoch 1/2 | Batch number 1300 | Discriminator Loss: 0.5599 | Generator Loss: 2.6385
Epoch 1/2 | Batch number 1400 | Discriminator Loss: 0.6325 | Generator Loss: 2.1403
Epoch 1/2 | Batch number 1500 | Discriminator Loss: 0.6957 | Generator Loss: 1.8090
Epoch 1/2 | Batch number 1600 | Discriminator Loss: 0.8445 | Generator Loss: 1.8782
Epoch 1/2 | Batch number 1700 | Discriminator Loss: 0.7705 | Generator Loss: 3.1730
Epoch 1/2 | Batch number 1800 | Discriminator Loss: 0.6110 | Generator Loss: 1.8407
Epoch 2/2 | Batch number 100 | Discriminator Loss: 0.8745 | Generator Loss: 1.0688
Epoch 2/2 | Batch number 200 | Discriminator Loss: 0.7655 | Generator Loss: 2.5965
Epoch 2/2 | Batch number 300 | Discriminator Loss: 0.7110 | Generator Loss: 1.6283
Epoch 2/2 | Batch number 400 | Discriminator Loss: 0.9081 | Generator Loss: 1.3563
Epoch 2/2 | Batch number 500 | Discriminator Loss: 0.6345 | Generator Loss: 1.8329
Epoch 2/2 | Batch number 600 | Discriminator Loss: 0.7091 | Generator Loss: 2.4666
Epoch 2/2 | Batch number 700 | Discriminator Loss: 0.8025 | Generator Loss: 2.0223
Epoch 2/2 | Batch number 800 | Discriminator Loss: 0.8424 | Generator Loss: 1.3803
Epoch 2/2 | Batch number 900 | Discriminator Loss: 0.8447 | Generator Loss: 1.2524
Epoch 2/2 | Batch number 1000 | Discriminator Loss: 0.7522 | Generator Loss: 1.9997
Epoch 2/2 | Batch number 1100 | Discriminator Loss: 1.1369 | Generator Loss: 1.3352
Epoch 2/2 | Batch number 1200 | Discriminator Loss: 0.9155 | Generator Loss: 1.3138
Epoch 2/2 | Batch number 1300 | Discriminator Loss: 0.6728 | Generator Loss: 1.2157
Epoch 2/2 | Batch number 1400 | Discriminator Loss: 0.7813 | Generator Loss: 1.6617
Epoch 2/2 | Batch number 1500 | Discriminator Loss: 0.8286 | Generator Loss: 2.5834
Epoch 2/2 | Batch number 1600 | Discriminator Loss: 0.4823 | Generator Loss: 2.3852
Epoch 2/2 | Batch number 1700 | Discriminator Loss: 0.5529 | Generator Loss: 2.3388
Epoch 2/2 | Batch number 1800 | Discriminator Loss: 0.5331 | Generator Loss: 2.6237

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [107]:
batch_size = 32
z_dim = 128
learning_rate = 0.0001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1 | Batch number 100 | Discriminator Loss: 0.5392 | Generator Loss: 2.3312
Epoch 1/1 | Batch number 200 | Discriminator Loss: 0.6187 | Generator Loss: 2.1933
Epoch 1/1 | Batch number 300 | Discriminator Loss: 0.6429 | Generator Loss: 1.9396
Epoch 1/1 | Batch number 400 | Discriminator Loss: 0.9329 | Generator Loss: 1.5080
Epoch 1/1 | Batch number 500 | Discriminator Loss: 0.7997 | Generator Loss: 2.5380
Epoch 1/1 | Batch number 600 | Discriminator Loss: 0.6543 | Generator Loss: 1.6428
Epoch 1/1 | Batch number 700 | Discriminator Loss: 0.9460 | Generator Loss: 1.2725
Epoch 1/1 | Batch number 800 | Discriminator Loss: 0.8572 | Generator Loss: 1.8676
Epoch 1/1 | Batch number 900 | Discriminator Loss: 1.0481 | Generator Loss: 1.3398
Epoch 1/1 | Batch number 1000 | Discriminator Loss: 1.1642 | Generator Loss: 1.1831
Epoch 1/1 | Batch number 1100 | Discriminator Loss: 1.2764 | Generator Loss: 1.0480
Epoch 1/1 | Batch number 1200 | Discriminator Loss: 1.0832 | Generator Loss: 1.1001
Epoch 1/1 | Batch number 1300 | Discriminator Loss: 1.2596 | Generator Loss: 1.0404
Epoch 1/1 | Batch number 1400 | Discriminator Loss: 0.9768 | Generator Loss: 1.4109
Epoch 1/1 | Batch number 1500 | Discriminator Loss: 0.9613 | Generator Loss: 1.3732
Epoch 1/1 | Batch number 1600 | Discriminator Loss: 0.7731 | Generator Loss: 1.6856
Epoch 1/1 | Batch number 1700 | Discriminator Loss: 0.8436 | Generator Loss: 1.1898
Epoch 1/1 | Batch number 1800 | Discriminator Loss: 1.1535 | Generator Loss: 1.0135
Epoch 1/1 | Batch number 1900 | Discriminator Loss: 1.2535 | Generator Loss: 1.0091
Epoch 1/1 | Batch number 2000 | Discriminator Loss: 1.0142 | Generator Loss: 1.2933
Epoch 1/1 | Batch number 2100 | Discriminator Loss: 1.0367 | Generator Loss: 1.3661
Epoch 1/1 | Batch number 2200 | Discriminator Loss: 1.0020 | Generator Loss: 1.2542
Epoch 1/1 | Batch number 2300 | Discriminator Loss: 1.0210 | Generator Loss: 0.8598
Epoch 1/1 | Batch number 2400 | Discriminator Loss: 1.0979 | Generator Loss: 0.9410
Epoch 1/1 | Batch number 2500 | Discriminator Loss: 1.0462 | Generator Loss: 1.1930
Epoch 1/1 | Batch number 2600 | Discriminator Loss: 1.1635 | Generator Loss: 0.9074
Epoch 1/1 | Batch number 2700 | Discriminator Loss: 0.9416 | Generator Loss: 1.1313
Epoch 1/1 | Batch number 2800 | Discriminator Loss: 0.9190 | Generator Loss: 1.6004
Epoch 1/1 | Batch number 2900 | Discriminator Loss: 1.1972 | Generator Loss: 1.0320
Epoch 1/1 | Batch number 3000 | Discriminator Loss: 1.0635 | Generator Loss: 1.0737
Epoch 1/1 | Batch number 3100 | Discriminator Loss: 0.9436 | Generator Loss: 1.2130
Epoch 1/1 | Batch number 3200 | Discriminator Loss: 1.1830 | Generator Loss: 1.2591
Epoch 1/1 | Batch number 3300 | Discriminator Loss: 0.9607 | Generator Loss: 1.4691
Epoch 1/1 | Batch number 3400 | Discriminator Loss: 1.1373 | Generator Loss: 1.0666
Epoch 1/1 | Batch number 3500 | Discriminator Loss: 1.1445 | Generator Loss: 1.1729
Epoch 1/1 | Batch number 3600 | Discriminator Loss: 1.0868 | Generator Loss: 1.2636
Epoch 1/1 | Batch number 3700 | Discriminator Loss: 0.9486 | Generator Loss: 1.1243
Epoch 1/1 | Batch number 3800 | Discriminator Loss: 1.0667 | Generator Loss: 1.0645
Epoch 1/1 | Batch number 3900 | Discriminator Loss: 1.1462 | Generator Loss: 0.8896
Epoch 1/1 | Batch number 4000 | Discriminator Loss: 1.0859 | Generator Loss: 0.9800
Epoch 1/1 | Batch number 4100 | Discriminator Loss: 0.9843 | Generator Loss: 1.1293
Epoch 1/1 | Batch number 4200 | Discriminator Loss: 1.0805 | Generator Loss: 1.2970
Epoch 1/1 | Batch number 4300 | Discriminator Loss: 1.2225 | Generator Loss: 0.8759
Epoch 1/1 | Batch number 4400 | Discriminator Loss: 0.9685 | Generator Loss: 1.1025
Epoch 1/1 | Batch number 4500 | Discriminator Loss: 1.0791 | Generator Loss: 1.1425
Epoch 1/1 | Batch number 4600 | Discriminator Loss: 0.8227 | Generator Loss: 1.2762
Epoch 1/1 | Batch number 4700 | Discriminator Loss: 1.0862 | Generator Loss: 0.9704
Epoch 1/1 | Batch number 4800 | Discriminator Loss: 1.0081 | Generator Loss: 1.2396
Epoch 1/1 | Batch number 4900 | Discriminator Loss: 0.9748 | Generator Loss: 1.3069
Epoch 1/1 | Batch number 5000 | Discriminator Loss: 0.8847 | Generator Loss: 1.2836
Epoch 1/1 | Batch number 5100 | Discriminator Loss: 0.8797 | Generator Loss: 1.4397
Epoch 1/1 | Batch number 5200 | Discriminator Loss: 0.8723 | Generator Loss: 1.3720
Epoch 1/1 | Batch number 5300 | Discriminator Loss: 0.8430 | Generator Loss: 1.8855
Epoch 1/1 | Batch number 5400 | Discriminator Loss: 0.8468 | Generator Loss: 1.5702
Epoch 1/1 | Batch number 5500 | Discriminator Loss: 1.3331 | Generator Loss: 0.9991
Epoch 1/1 | Batch number 5600 | Discriminator Loss: 1.1513 | Generator Loss: 0.9221
Epoch 1/1 | Batch number 5700 | Discriminator Loss: 1.0890 | Generator Loss: 1.1305
Epoch 1/1 | Batch number 5800 | Discriminator Loss: 1.0647 | Generator Loss: 1.0216
Epoch 1/1 | Batch number 5900 | Discriminator Loss: 0.9347 | Generator Loss: 1.1386
Epoch 1/1 | Batch number 6000 | Discriminator Loss: 0.8269 | Generator Loss: 1.7399
Epoch 1/1 | Batch number 6100 | Discriminator Loss: 0.8177 | Generator Loss: 1.5049
Epoch 1/1 | Batch number 6200 | Discriminator Loss: 0.8933 | Generator Loss: 1.2767
Epoch 1/1 | Batch number 6300 | Discriminator Loss: 0.9186 | Generator Loss: 1.3384

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.